2010
DOI: 10.2478/v10006-010-0009-3
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A complete gradient clustering algorithm formed with kernel estimators

Abstract: The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions rega… Show more

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Cited by 34 publications
(19 citation statements)
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“…Meanwhile these factors more or less compensate for each other in dense areas, thereby having small influence on the detection of clusters located there. Detailed information on the CGCA procedures and their influences on the clustering results is described in [9].…”
Section: Procedures Of the Cgcamentioning
confidence: 99%
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“…Meanwhile these factors more or less compensate for each other in dense areas, thereby having small influence on the detection of clusters located there. Detailed information on the CGCA procedures and their influences on the clustering results is described in [9].…”
Section: Procedures Of the Cgcamentioning
confidence: 99%
“…Accomplished studies showed that digital image processing techniques commonly used in multivariate analysis give reliable results in classification process [13,15,17]. In this paper, the algorithm proposed in [9] will be used to identify wheat varieties, using their main geometric features.…”
Section: Introductionmentioning
confidence: 99%
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“…with many practical applications, see for example [14] and many others. Constructing the most adequate PDF from the observed data is still an important and interesting research problem, especially for large datasets.…”
Section: Introductionmentioning
confidence: 99%